O-glycosylation of mammalian proteins is one of the important posttranslational modifications. We applied a support vector machine (SVM) to predict whether Ser or Thr is glycosylated, in order to elucidate the O-glycosylation mechanism. O-glycosylated sites were often found clustered along the sequence, whereas other sites were located sporadically. Therefore, we developed two types of SVMs for predicting clustered and isolated sites separately. We found that the amino acid composition was effective for predicting the clustered type, whereas the site-specific algorithm was effective for the isolated type. The highest prediction accuracy for the clustered type was 74%, while that for the isolated type was 79%. The existence frequency of amino acids around the O-glycosylation sites was different in the two types: namely, Pro, Val and Ala had high existence probabilities at each specific position relative to a glycosylation site, especially for the isolated type. Independent component analyses for the amino acid sequences around O-glycosylation sites showed the position-specific existences of the identified amino acids as independent components. The O-glycosylation sites were preferentially located within intrinsically disordered regions of extracellular proteins: particularly, more than 90% of the clustered O-GalNAc glycosylation sites were observed in intrinsically disordered regions. This feature could be the key for understanding the non-conservation property of O-glycosylation, and its role in functional diversity and structural stability.

The seventh novel human infecting Betacoronavirus that causes pneumonia (2019 novel coronavirus, 2019-nCoV) originated in Wuhan, China. The evolutionary relationship between 2019-nCoV and the other human respiratory illness-causing coronavirus is not closely related. We sought to characterize the relationship of the translated proteins of 2019-nCoV with other species of Orthocoronavirinae. A phylogenetic tree was constructed from the genome sequences. A cluster tree was developed from the profiles retrieved from the presence and absence of homologs of ten 2019-nCoV proteins. The combined data were used to characterize the relationship of the translated proteins of 2019-nCoV to other species of Orthocoronavirinae. Our analysis reliably suggests that 2019-nCoV is most closely related to BatCoV RaTG13 and belongs to subgenus Sarbecovirus of Betacoronavirus, together with SARS coronavirus and Bat-SARS-like coronavirus. The phylogenetic profiling cluster of homolog proteins of one annotated 2019-nCoV protein against other genome sequences revealed two clades of ten 2019-nCoV proteins. Clade 1 consisted of a group of conserved proteins in Orthocoronavirinae comprising Orf1ab polyprotein, Nucleocapsid protein, Spike glycoprotein, and Membrane protein. Clade 2 comprised six proteins exclusive to Sarbecovirus and Hibecovirus. Two of six Clade 2 nonstructural proteins, NS7b and NS8, were exclusively conserved among 2019-nCoV, BetaCoV_RaTG, and BatSARS-like Cov. NS7b and NS8 have previously been shown to affect immune response signaling in the SARS-CoV experimental model. Thus, we speculated that knowledge of the functional changes in the NS7b and NS8 proteins during evolution may provide important information to explore the human infective property of 2019-nCoV.

Motivation: The enzyme nomenclature system, commonly known as the enzyme commission (EC) number, plays a key role in classifying and predicting enzymatic reactions. However, numerous reactions have been described in various pathways that do not have an official EC number, and the reactions are not expected to have an EC number assigned because of a lack of articles published on enzyme assays. To predict the EC number of a non-classified enzymatic reaction, we focus on the structural similarity of its substrate and product to the substrate and product of reactions that have been classified. Results: We propose a new method to assign EC numbers using a maximum common substructure algorithm, mutual information and a support vector machine, termed the Enzyme COmmission numbers Handler (ECOH). A jack-knife test shows that the sensitivity, precision and accuracy of the method in predicting the first three digits of the official EC number (i.e. the EC sub-subclass) are 86.1%, 87.4% and 99.8%, respectively. We furthermore demonstrate that, by examining the ranking in the candidate lists of EC sub-subclasses generated by the algorithm, the method can successfully predict the classification of 85 enzymatic reactions that fall into multiple EC sub-subclasses. The better performance of the ECOH as compared with existing methods and its flexibility in predicting EC numbers make it useful for predicting enzyme function. Availability: ECOH is freely available via the Internet at http://www.bioinfo.sk.ritsumei.ac.jp/apps/ecoh/. This program only works on 32-bit Windows. Contact:yukako@sk.ritsumei.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

The mammalian CIP/KIP family proteins are intrinsically disordered proteins (IDPs) that can regulate various cellular processes. However, many reports have shown that IDPs generally evolve more rapidly than ordered proteins. Here, to elucidate the functional adaptability of CIP/KIP proteins in vertebrate, we analysed the rates of evolution in relation to their structural and sequence properties and predicted the post-translational modification based on the sequence data. The results showed that CIP/KIP proteins generally could maintain their function through evolution in the vertebrate. Basically, the disordered region that acts as a flexible linker or spacer has a conserved propensity for structural disorder and a persistent, fast rate of amino acid substitution, which could result in a significantly faster rate of evolution compared to the ordered proteins. Describing the pattern of structural order-disorder evolution, this study may give an insight into the well-known characteristics of IDPs in the evolution of CIP/KIP proteins.